Within the vehicle industry, object detection systems are used for detecting objects in the environment surrounding the vehicle, such as solid or movable obstacles, other vehicles, etc. Normally, the object would be detected via one or several of a multiple of object detection sensors of the vehicle. Examples of such object detection sensors are radars, lasers, and color cameras. Also, the object detection system might include enlightenment devices or display devices that make a visual examination of the surrounding environment possible. The object detection system may connected to other systems for automatic collision warning, enlightenment steering, etc.
It has also been proposed that map databases including information regarding road characteristics such as curvature, speed limit, etc. could be used together with object detection systems, for example for determining the nature of an obstacle approached by a vehicle. (Otherwise, systems using geographic map databases are commonly used as navigation aid systems for drivers of vehicles.)
U.S. Pat. No. 6,161,071 (Shuman) describes a computing architecture for a motorized land-based vehicle. The computing architecture includes a data network comprised of a plurality of interconnected processors, a first group of sensors responsive to environmental conditions around the vehicle, a second group of sensors responsive to the vehicle's hardware systems, and a map database containing data that represent geographic features in the geographic area around the vehicle. A vehicle-environment modelling program, executed on the data network, uses the outputs from the first and second group of sensors and the map database to provide and continuously update a data model that represents the vehicle and the environmental around the vehicle, including geographic features, conditions, structures, objects and obstacles around the vehicle. Vehicle operations programming applications, executed on the data network, use the data model to determine desired vehicle operation in the context of the vehicle's environment. The vehicle operations programming applications may include adaptive cruise control, automated mayday, and obstacle and collision warning systems, among others. EP 1 098 168 (Chojnacki) describes a system for collecting data for automatic generation of shape and curvature data of roads for a geographic database. The information stored in the database could for example be used for headlight aiming or curve warning.
JP 10 221 451 (Yoshiyuki) describes a system for identifying whether an object detected by a vehicle mounted radar is an obstacle or not.
In these prior systems, information retrieved from the object detection system is compared to the information in the map database for updating of the database or identification of the detected object.
However, with existing object detection systems (with or without connection to map databases) there are problems resulting from the limited resolution and detection area of the sensors of the system. Most available sensors can only acquire a clear picture of the road ahead in a relatively small area. In a broad area, only a rough picture of the road is available. Thus the detection capacity of the system is limited.
The resolution could theoretically be increased by selecting more expensive hardware, such as camera systems performing saccadic movements, or simply by multiplying the number of sensors. However, these options result in severely increased costs for the system, making the system less affordable to the customer.
Since most object detection systems operate in real-time or close to real-time, the processing of the data received from the object detection sensors must be fast. However, the data flow to be processed from, for example, radars, lasers, and especially color cameras is very extensive. A known method to speed up the processing is to allocate more processing capacity to areas in the complete picture frame of the sensors where objects were recognised in a previous picture frame than to areas where no object was detected. However, this method has the disadvantage that recognition of objects appearing for the first time in a new area of the picture frame will be delayed.
Another prior known way of allocating the attention of the object detection sensors in a picture frame is using input of other sensors of the vehicle, such as sensors indicating the vehicle speed, yaw rate, and pitch. These parameters could be combined to estimate the most favorable allocation of the detection sensors.
An object of the present invention is to provide a system for enhancing the function of an object detection system.